The ability to gather and manipulate data is increasingly important in today's society. Small businesses and large corporations alike are aware that compiling, manipulating and reviewing data can provide valuable information about their present and future customers. Whether it is data on product usage or data on seasonal trends in customer purchases, the need to locate, gather on demand, and manipulate data is important. Even computer software designers are aware that the ability to manipulate and present data may provide valuable insight to business owners and managers. To that end, many software designers have developed software applications capable of manipulating and presenting data in a variety of ways to help improve data compilation and manipulation.
Companies have long recognized the need for gathering and storing data relating to its customers, suppliers etc. At one time, this data was kept in boxes and stored in rooms. When storage space for data became cumbersome, data was often transferred to mainframe computers and other like electronic data storage devices. These storage devices allow massive electronic data files to be stored in one location. However, when mainframes and other like devices became the preferred method of storing large volumes of data, the need and the technology to perform data manipulations to present data in a variety of ways was not as prevalent as it is today. Instead, the mainframe was often used to archive data that was important but infrequently accessed.
As times changed so did the purpose of storing data on a mainframe and the frequency with which this data was accessed. No longer is data stored on a mainframe primarily because it is historic information that needs to be archived. To the contrary, data is sometimes stored on a mainframe because it is so voluminous that it would occupy too much space on a local area network server, a wide area network server or a desktop computer hard drive. The increased frequency with which data on a mainframes is needed creates problems with efficiently providing desktop software applications access to the data when it is stored on a remote mainframe or other like data storage device.
To obtain information stored in data files on a mainframe, end-users often have to print the entire data file from the mainframe. Oftentimes, the data manipulation performed by the desktop software application does not require all the data stored in the data file on the mainframe. The end-user is, therefore, forced to manually filter through the voluminous data file, pinpoint the data needed and manually create a data feed file for use by the desktop software application. Thus, there is an obvious need for the capability to automatically and electronically select the data stored on the mainframe and generate a data feed file that is accessible to desktop software applications for data manipulation and presentation.